Diabetic retinopathy is a widespread eye disease that may cause blindness in diabetic patients. Often patients are not aware of the disease until its late stages, thus annual screening of patients for possible diabetic retinopathy is recommended.
In the screening, microaneurysms (MAs) are one of the earliest visible lesions in diabetic retinopathy, and are therefore an important pathology to be detected and followed closely. The number, density and locations of MAS are important factors to quantify the progression of diabetic retinopathy.
MAs are saccular outpouchings of the retinal capillaries. Their size ranges from 10 μm to 100 μm, and may be assumed always to be less 125 μm. As capillaries are too thin to be visible in a digital fundus image, MAs appear to be isolated patterns that are disconnected from the blood vessels.
Hemorrhages are blood leaking from MAs and deposited in the retina. Small dotted hemorrhages are often hard to visually differentiate from MAs. Consistent with most published work in this area, no distinction is made herein between small dotted hemorrhages and MAs.
FIG. 1 is an illustration of various structures contained in a digital fundus image 100. The white rectangular region in the right image is zoomed as the left image for better visibility. The ocular fundus image 100 contains several MAs 110 and a hemorrhage 120. In addition, several ocular structures appear in the image, including an optic disk 130, hard exudate 140 and the macula 150.
Manual identification of MAs in a fundus image is time-consuming and subjected to inter- and intra-operator variability. Screening a large number of diabetic patients annually poses a huge workload for ophthalmologists. A system is needed wherein MAs and other lesions are automatically and accurately detected, and only suspicious cases are referred to ophthalmologists for further evaluation and treatment.
Most existing MA detection techniques were developed for use with a fluorescein angiogram, which is an image of the ocular fundus obtained after a fluorescent dye is injected into a patient's body and passed through the blood vessels of the retina. MAs are thereby highlighted in fluorescein angiograms, making MAs detection easier.
In recent years, digital ocular fundus images, which do not require dye injection, are more commonly used in screenings. In a digital fundus image, MAs are small dark red dots several pixels in size, depending on image resolution. Although many of the techniques developed for fluorescein angiograms can be directly applied to digital fundus images, care must be taken to account for the weaker contrast of MAs to the surrounding pixels. The present invention addresses MAs detection using a digital ocular fundus image.
A number of algorithms have been proposed for MAs detection in mass screening. Most of them process digital fundus images globally without a mechanism to take into account local properties and changes in the image. Performance of those algorithms is often susceptible to non-uniform illumination and to the locations of MAs in different retinal regions. To keep sensitivity at a relatively high level, a low threshold value must be applied to the entire image globally, resulting in a much lower specificity in MAs detection. Post-processing steps, such as feature extraction and classification, must be implemented to improve the specificity at the cost of sensitivity.
A widely used scheme 200 for MAs detection is shown in FIG. 2. The sequence of operations includes image preprocessing (step 210), global thresholding of the enhanced image (step 220), region growing (step 230), feature extraction (step 240) and classification (step 250) to discriminate true MAs from false detections. That technique has achieved some degree of success in MA detection; however, several factors constrain further improvement of the detection accuracy.
For example, local properties of the retina and inhomogeneous illumination of different regions are not considered in that framework. Thus, a global processing method often generates a considerable number of false detections. Some preprocessing techniques, such as shade correction, can ease the severity of inhomogeneous imaging conditions; however, the problems associated with global thresholding still exist.
The region grow, feature extraction and classification steps can remove some false detections, but those steps may also introduce additional errors. For example, region growing for small objects such as MAs is not very reliable. The shape feature in MAs detection is essential to classification; however, due to the irregular shape of MAs, the classifier is usually trained to accept shapes varying in a large range, which leads to misclassification.
Those issues exist for MAs detection using fluorescein angiograms, and are likely to be more severe with digital fundus images, where MAs appear to have much weaker contrast with neighboring pixels. In addition, all parameters in the sequential procedure 200 are coupled and affect each other; i.e., the parameters in a later processing step must be adjusted according to the output of the previous one. As a result, performance is more sensitive to parameter adjustment, and is less robust.
Another method based on normalized cuts has been proposed for MA detection. Several factors, however, may hinder its success in real applications. Its performance is sensitive to the number of segments selected, and the computational complexity can be as high as O(n3), where n is the number of pixels. The method therefore becomes impractical with digital fundus images, which are normally 1024×1280 pixels.
There is presently a need to provide a method and system for reliably detecting MAs in a digital ocular fundus image. To the inventors' knowledge, there is currently no such technique available.